Keep it simple and sufficient (do not multiply it unnecessarily)

Make it intuitive and self-explanatory

Make it easily discoverable and accessible

Make it unobtrusive yet obvious

Make it relevant to context

Make it available with all data products

Make it universally applicable and extensible

Treat it as a product

Support appropriate standards (but not slavishly)

Document and promote it

General Metadata Best Practices

Mapping source metadata to integrated metadata

Browsing and searching datasets

Assisting the user in selecting and filtering data

Providing method information

Indicating exceptional events

Flagging at the dataset, sample, and observation level

Facilitating interoperability with other data systems

Metadata uses

Data Import System

Transformation MappingSchemaValidation

Source Object Extraction

· Identify and extract the unique Site, Parameter, Method, and Flag values that exist in the source data but not in the Integrated Database;

· Create new records in the Core database for any new Sites, Parameters, Methods, or Flags found in the source data;

· Transform the data from its source schema into the VIEWS integrated schema using the Integrated Schema Translation (ICS) table;

· Apply appropriate DB integrity constraints to verify the success and accuracy of the transformations;

· Map source codes and values to integrated codes and values using the Integrated Codes Translation (ICT) table;

· Load the results into the production database and validate the data using a series of row and column checksums and record counts;

Metadata Import System

Data Acquisition System

SOURCE

CORESOURCE

· Import source data from its original medium into staging tables in the Source database;

· Apply integrity constraints to the source schema tables to identify any issues in the source data;

· Store the source schema information in the Integrated Schema Translation (IST) table for later use during the transformation stage. CORE

Source Schema Extraction

Source Validation

Source Import

Sites

Parameters

Methods

Flags

Code Mapping

SOURCE

IST

ICT

IST ICT

CORE

AccuracyValidation

Dataset Discovery

Monitoring Site

Exploration

Glossary

And

Code Key

Mouse-over metadata lookup

Inter-domain “Rosetta Stone”

Exceptional Event Metadata

Exceptional event metadata (such as fires) is stored and dynamically associated with data at run time in order to better inform the user about the context of the data.

“Bisquit” fire impacting Crater Lake in 2002

IMPROVE Data Advisories

IMPROVE Data Advisories document interesting findings from the IMPROVE database such as data anomalies, potential problems, and new uses for the IMPROVE data.

These advisories are stored as metadata in the VIEWS database and dynamically associated with data and products. When a user selects data, the systems checks for any advisories relevant to the data selected and attaches them to the output.

Data Advisory Schema

The Data Advisory table contains information that allows each advisory to be dynamically associated with (context-relevant to) the data and products that a user requests.

Metadata is relevant at several “levels” and in several contexts: Dataset description Sample and observation flagging Names and codes Data exchange and interoperability protocols (WCS, WMS, WFS, etc)

What is a minimum set of metadata for the air quality community?

Is there an existing standard that completely fits the bill?

How can we design and refine a minimum set for community use?

How could we solicit input during the design of this metadata?

How would we promote and motivate acceptance of this set?

How would this metadata “standard” facilitate interoperability?

Would it be worth the effort?

Closing thoughts